A Cross-Voxel Exchange Model for the Non-invasive Imaging of Tracer Transport in Tumours
Bibliographic record
Abstract
Tumours exhibit abnormal interstitial structures and vasculature function often leading to an impaired and heterogeneous drug delivery. Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the quantification of tracer perfusion based on contrast enhancement measured with non-invasive imaging techniques. In this thesis, a mathematical framework was developed to provide a comprehensive description of tracer extravasation as well as advection and diffusion based on cross-voxel tracer kinetics. The focus of the first part is on examining the assumptions made by Tofts Model (TM), a widely employed predecessor, and building upon the findings to develop an advanced Cross-Voxel Exchange Model (CVXM). The second part employs in silico datasets quantifying the roles of convection and diffusion in tracer transport (which TM ignores) to investigate the validity of Tofts’ perfusion parameters compared to CVXM. In the third part, transport parameters were derived from the dynamic contrast-enhanced magnetic resonance images of human cervical carcinoma xenografts by using CVXM. The resulting velocity flows, tracer diffusivities and extravasation parameters were employed to explain the heterogeneous distribution of the tracer across the tumour and its accumulation at the periphery. Finally, a minimum scan time was advised for the pre-clinical datasets that renders informative estimations of the transport parameters. Concluding, the new mathematical framework, based on CVXM, can determine transport metrics characterizing the exchange of tracer between the vasculature and the tumour tissue, potentially leading to its clinical application in personalized treatment planning and its employment in drug development research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".